Alessia SAGGESE | MOBILE ROBOTS FOR CRITICAL MISSIONS
Alessia SAGGESE MOBILE ROBOTS FOR CRITICAL MISSIONS
cod. 0622700103
MOBILE ROBOTS FOR CRITICAL MISSIONS
| 0622700103 | |
| DEPARTMENT OF INFORMATION AND ELECTRICAL ENGINEERING AND APPLIED MATHEMATICS | |
| EQF7 | |
| COMPUTER ENGINEERING | |
| 2024/2025 |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2022 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/05 | 3 | 24 | LESSONS | |
| ING-INF/05 | 1 | 8 | LAB | |
| ING-INF/05 | 2 | 16 | EXERCISES |
| Objectives | |
|---|---|
| THE GOAL OF THE COURSE IS TO PROVIDE THE STUDENT WITH THE ARCHITECTURAL, METHODOLOGICAL, AND DESIGN ELEMENTS FOR THE CONSTRUCTION OF INTELLIGENT ROBOTS CAPABLE OF MOVING AUTONOMOUSLY IN INDOOR ENVIRONMENTS. IN PARTICULAR, THE COURSE FOCUSES ON ASPECTS RELATED TO MAP REPRESENTATION, MAP BUILDING, ROBOT LOCALIZATION, NAVIGATION AND OBSTACLE AVOIDANCE ALGORITHMS. KNOWLEDGE AND UNDERSTANDING THE COURSE PRESENTS THE METHODOLOGIES TO ALLOW THE AUTONOMOUS MOVEMENT OF THE ROBOT IN INDOOR ENVIRONMENTS, WHERE THE MAP AND THE ELEMENTS PRESENT WITHIN THE SCENE (FOR INSTANCE THE OBSTACLES) AND THE RELATED POSITION ARE NOT KNOWN A PRIORI. APPLYING KNOWLEDGE AND UNDERSTANDING ABILITY TO DESIGN AND IMPLEMENT SOLUTIONS TO INTELLIGENT ROBOT PROBLEMS, BY CHOOSING AND APPLYING THE APPROPRIATE METHODS PRESENTED IN THE COURSE, AND THE SOFTWARE ENVIRONMENTS SPECIFICALLY DEVISED FOR COGNITIVE ROBOTICS. |
| Prerequisites | |
|---|---|
| IN ORDER TO ACHIEVE THE GOALS OF THE COURSE, THE KNOWLEDGE OF THE C AND PYTHON PROGRAMMING LANGUAGE IS REQUIRED. |
| Contents | |
|---|---|
| DIDACTIC UNIT 1: INTRODUCTION TO MAP BASED NAVIGATION AND MAP BASED REPRESENTATIONS (LECTURE/PRACTICE/LABORATORY HOURS 10/5/0) - 1 (3 HOURS LECTURE): INTRODUCTION TO THE COURSE - 2 (2 HOURS LECTURE): SENSORS FOR MOBILE ROBOTS NAVIGATION: IMU, GPS, LASER SCAN - 3 (3 HOURS LECTURE): BEHAVIOR BASED VS MAP BASED NAVIGATION. DIFFERENTIAL ROBOT: MOTION MODEL - 4 (2 HOURS LECTURE): MAP BASED REPRESENTATIONS - 5 (3 HOURS PRACTICE): INTRODUCTION TO GAZEBO SIMULATOR - 6 (2 HOURS PRACTICE): RVIZ. SENSORS DATA ACQUISITION AND ROBOT INTERACTION IN GAZEBO KNOWLEDGE AND UNDERSTANDING: ACQUIRING THE KNOWLEDGE RELATED TO THE SENSORS NECESSARY FOR THE AUTONOMOUS NAVIGATION OF A ROBOT AND TO THE REPRESENTATIONS OF THE MAPS; KNOW THE GAZEBO SIMULATOR AND THE LIBRARIES TO MOVE THE ROBOT MANUALLY APPLYING KNOWLEDGE AND UNDERSTANDING: KNOWING HOW TO MOVE THE ROBOT INSIDE THE GAZEBO SIMULATOR AND BEING ABLE TO ACQUIRE DATA FROM THE SENSORS IN SIMULATION USING THE ROS FRAMEWORK DIDACTIC UNIT 2: MAP BASED LOCALIZATION ALGORITHMS (LECTURE/PRACTICE/LABORATORY HOURS 5/3/0) - 7 (3 HOURS LECTURE): MAP BASED LOCALIZATION: AN OVERVIEW. MARKOV BASED LOCALIZATION. THEORY AND EXAMPLE - 8 (2 HOURS LECTURE): 2D KALMAN FILTER. KALMAN FILTER FOR LOCALIZATION. KALMAN FILTER FOR DIFFERENTIAL ROBOT - 9 (3 HOURS PRACTICE): LOCALIZATION WITH KALMAN FILTER IN GAZEBO KNOWLEDGE AND UNDERSTANDING: KNOWING THE LOCALIZATION ALGORITHMS BASED ON KALMAN FILTER AND PARTICLE FILTERING APPLYING KNOWLEDGE AND UNDERSTANDING: KNOWING HOW TO DESIGN AND IMPLEMENT LOCALIZATION ALGORITHMS INTEGRATED INTO GAZEBO. KNOWING HOW TO USE ROS NODES ALREADY AVAILABLE IN GAZEBO FOR LOCALIZATION (EKF, AMCL) DIDACTIC UNIT 3: SIMULTANEOUS LOCALIZATION AND MAPPING ALGORITHMS (LECTURE/PRACTICE/LABORATORY HOURS 5/2/0) - 10 (2 HOURS LECTURE): AUTONOMOUS MAP BUILDING. SLAM ALGORITHM. EKF SLAM - 11 (3 HOURS LECTURE): PARTICLE FILTERING. MONTECARLO LOCALIZATION BASED ON PARTICLE FILTERING. PF BASED SLAM - 12 (2 HOURS PRACTICE): SLAM IN GAZEBO KNOWLEDGE AND UNDERSTANDING: KNOWING THE SLAM ALGORITHMS BASED ON EFK AND PF APPLYING KNOWLEDGE AND UNDERSTANDING: KNOWING HOW TO USE AND CONFIGURE THE ALGORITHMS ALREADY AVAILABLE IN GAZEBO FOR SLAM (GMAPPING) DIDACTIC UNIT 4: NAVIGATION: PATH PLANNING AND OBSTACLE AVOIDANCE ALGORITHMS (LECTURE/PRACTICE/LABORATORY HOURS 5/5/8) - 13 (3 ORE ESERCITAZIONE): FROM GAZEBO SIMULATOR TO TURTLEBOT ROBOT. - 14 (2 HOURS LECTURE): NAVIGATION. PATH PLANNING ALGORITHMS. ROAD MAP, CELL DECOMPOSITION - 15 (3 HOURS LECTURE): OBSTACLE AVOIDANCE ALGORITHMS - 16 (2 HOURS PRACTICE): NAVIGATION, PATH PLANNING AND OBSTACLE AVOIDANCE - 17 (3 HOURS LABORATORY): FINAL PROJECT - 18 (2 HOURS LABORATORY): FINAL PROJECT - 19 (3 HOURS LABORATORY): FINAL PROJECT KNOWLEDGE AND UNDERSTANDING: KNOWING THE PATH PLANNING ALGORITHMS (BASED ON ROAD MAP, CELL DECOMPOSITION) AND OBSTACLE AVOIDANCE ALGORITHMS APPLYING KNOWLEDGE AND UNDERSTANDING: KNOWING HOW TO USE AND CONFIGURE THE ALGORITHMS ALREADY AVAILABLE IN GAZEBO FOR NAVIGATION. KNOWING HOW TO INTEGRATE ALL THE ALGORITHMS STUDIED ON THE TURTLEBOT ROBOTIC PLATFORM (NOT IN SIMULATION) TOTAL LECTURE/PRACTICE/LABORATORY HOURS 25/15/8 |
| Teaching Methods | |
|---|---|
| THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS. DURING THE IN-CLASS EXERCITATIONS THE STUDENTS ARE DIVIDED IN TEAMS AND ARE ASSIGNED SOME PROJECT-WORKS TO BE DEVELOPED ALONG THE DURATION OF THE COURSE. THE PROJECTS INCLUDE ALL THE CONTENTS OF THE COURSE AND IS ESSENTIAL BOTH FOR THE ACQUISITION OF THE RELATIVE ABILITIES AND COMPETENCES, AND FOR DEVELOPING AND REINFORCING THE ABILITY TO WORK IN A TEAM. IN THE LABORATORY EXERCITATIONS THE STUDENTS IMPLEMENT THE ASSIGNED PROJECTS USING ROS. IN ORDER TO PARTICIPATE TO THE FINAL ASSESSMENT AND TO GAIN THE CREDITS CORRESPONDING TO THE COURSE, THE STUDENT MUST HAVE ATTENDED AT LEAST 70% OF THE HOURS OF ASSISTED TEACHING ACTIVITIES. |
| Verification of learning | |
|---|---|
| THE EXAM TEST IS AIMED AT ASSESSING AS A WHOLE: THE KNOWLEDGE AND ABILITY TO UNDERSTAND THE CONCEPTS PRESENTED IN THE COURSE; THE ABILITY TO APPLY SUCH KNOWLEDGE TO SOLVE PROGRAMMING PROBLEMS THAT REQUIRE THE USE OF AUTONOMOUS ROBOTIC NAVIGATION TECHNIQUES; INDEPENDENT JUDGMENT, COMMUNICATION SKILLS AND THE ABILITY TO LEARN. STUDENTS, DIVIDED INTO TEAMS, MUST DESIGN AND CREATE THE SOFTWARE FOR A MOBILE ROBOTICS APPLICATION. THE PROJECT AND METHODOLOGICAL CHOICES MADE ARE THE OBJECT OF EVALUATION, TOGETHER WITH THE RESULT OBTAINED BY THE TEAM DURING THE DISCUSSION OF THE PROJECT. THE FINAL TEST IS DIVIDED INTO TWO PHASES: THE FIRST CONSISTS OF A DEMONSTRATION AND A DISCUSSION OF THE COMPLETED PROJECT. A SECOND PHASE INCLUDES A WRITTEN TEST RELATING TO ALL THE TOPICS COVERED DURING THE COURSE. THE PURPOSE IS TO ASSESS THE ACQUIRED KNOWLEDGE AND UNDERSTANDING ABILITY, THE ABILITY TO APPLY KNOWLEDGE, INDEPENDENT JUDGMENT AND THE ABILITY TO WORK IN A GROUP. IN THE FINAL ASSESSMENT, EXPRESSED IN THIRTIETHS, THE EVALUATION OF THE PROJECT AND THE RELATED DISCUSSION WILL WEIGH FOR 50%, THE WRITTEN EXAM WILL WEIGH FOR THE REMAINING 50%. PRAISE MAY BE AWARDED TO STUDENTS WHO DEMONSTRATE THE ABILITY TO APPLY THE KNOWLEDGE ACQUIRED INDEPENDENTLY EVEN IN CONTEXTS DIFFERENT THAN THOSE PROPOSED IN THE COURSE |
| Texts | |
|---|---|
| INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS. ROLAND SIEGWART, ILLAH R. NOURBAKHSH, A BRADFORD BOOK, THE MIT PRESS, 2004 THE TEACHING MATERIAL IS AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS. |
| More Information | |
|---|---|
| THE COURSE IS HELD IN ENGLISH |
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